Journal: International Dental Journal

Article Title: Obesity as a Determinant of Periodontal Therapy Outcomes: Insights on Oxidative and Endoplasmic Reticulum Stress Pathways

doi: 10.1016/j.identj.2026.109472

Figure Lengend Snippet: Oxidative stress markers in the study population, stratified by obesity presence, before and after non-surgical periodontal treatment. Total ROS (A and G), cytosolic superoxide (B and H) and mitochondrial superoxide (C and I) in leukocytes, and protein levels of SOD1 (D and J) in PBMCs normalized to the loading control actin with corresponding WB images (E), and serum total antioxidant capacity (F and K). Data are presented as box and whisker plots and were compared using two-factor analysis of variance (ANOVA) followed by post hoc tests. ** P < .01 when comparing baseline vs 12 wk. # P < .05 when comparing groups with vs without obesity. DCFH, diclorodihidrofluoresceína; dHE, dihydroethidium; MitoSOX, Mitochondrial Superoxide Indicator; ns, not significant; PBMCs, peripheral blood mononuclear cells; ROS, reactive oxygen species; SOD1, superoxide dismutase 1.

Article Snippet: Peripheral blood mononuclear cells (PBMCs) comprising monocytes and lymphocytes were isolated from EDTA-collected blood using the MACSprep PBMC Isolation Kit (Miltenyi Biotec, Teterow, Germany), and 5 × 106 cells were used for protein extraction, quantification, and Western blot (WB) analysis.

Techniques: Control, Whisker Assay

Journal: International Dental Journal

Article Title: Obesity as a Determinant of Periodontal Therapy Outcomes: Insights on Oxidative and Endoplasmic Reticulum Stress Pathways

doi: 10.1016/j.identj.2026.109472

Figure Lengend Snippet: Evaluation of UPR markers in PBMCs from study population, stratified by presence of obesity, before and after non-surgical periodontal treatment. Relative protein expression of GRP78 (A and G), ATF6 (B and H), CHOP (C and I), IRE1α (D and J), p-eIF2α (E and K), relative mRNA expression of sXBP1 (F and L) in PBMCs normalized to the loading control actin and representative WB images (M). Data are presented as box and whisker plots and were compared using two-factor analysis of variance (ANOVA) followed by post hoc tests. * P < .05; ** P < .01 when comparing baseline vs 12 wk. ATF6, activating transcription factor 6; BMI, body mass index; CHOP, C/EBP homologous protein; GRP78, Glucose-regulated protein 78; IRE1α, Inositol requiring enzyme 1α; PBMCs, peripheral blood mononuclear cells; p-eIF2α, Phosphorylated eukaryotic translation initiation factor 2 subunit; sXBP1 , spliced Xbox binding protein 1 gene.

Article Snippet: Peripheral blood mononuclear cells (PBMCs) comprising monocytes and lymphocytes were isolated from EDTA-collected blood using the MACSprep PBMC Isolation Kit (Miltenyi Biotec, Teterow, Germany), and 5 × 106 cells were used for protein extraction, quantification, and Western blot (WB) analysis.

Techniques: Expressing, Control, Whisker Assay, Binding Assay

Journal: iScience

Article Title: Profiling biological effects of microbiome metabolites via machine learning

doi: 10.1016/j.isci.2026.115282

Figure Lengend Snippet: Machine learning model validation using external datasets and prospective in vitro experiments (A) Odds ratios of predicted positives in external validation sets relative to the microbiome background. The HIA model (GUTSY, n = 50) and BBB model (NIAGADS, n = 151) showed significant enrichment (ORs = 6.0 and 2.3, respectively; Fisher’s exact test, p = 2 × 10 −4 and p = 5 × 10 −4 , respectively). (B–D) Prospective in vitro validation of drug-induced liver injury prediction in HepG2 cells. (C) Predicted liver-toxic metabolites exhibited dose-dependent, significant cytotoxic effects. (D) Predicted liver-safe metabolites showed no significant cytotoxic effect ( p > 0.05) at any of the tested concentrations up to 1 mM (unpaired t test; N = 3, n = 3). (E) Confusion matrix for the prospective in vitro validation. (F) Evaluation of performance of machine learning models predicting IL-8 secretion stimulation. The RF model outperformed each of the other models (∗ p ≤ 0.05, ∗∗ p ≤ 0.01, ∗∗∗ p ≤ 0.001, ∗∗∗∗ p ≤ 0.0001). (G) Chemical structures of top predicted candidates, spermine and spermidine. (H) Results from the IL-8 secretion assay using human PBMCs, evaluating the effect of seven microbiome metabolites at 100 μM ( N = 1, n = 3). Metabolites stimulating IL-8 are shown in pink, and metabolites with no IL-8 secretion are shown in blue (∗ p ≤ 0.05, ∗∗ p ≤ 0.01, ∗∗∗ p ≤ 0.001, ∗∗∗∗ p ≤ 0.0001). (I) IL-8 stimulation by spermine and spermidine at different concentrations ( N = 2, n ≥ 2). Data are represented as the mean ± standard deviation.

Article Snippet: Primary human peripheral blood mononuclear cell (PBMC) , ATCC , PCS-800-011, Lot 8032322.

Techniques: Biomarker Discovery, In Vitro, Standard Deviation